This repository provides models and supporting code associated with AudioSet, a dataset of over 2 million human-labeled 10-second YouTube video soundtracks, with labels taken from an ontology of more than 600 audio event classes.

AudioSet was released in March 2017 by Google's Sound Understanding team to provide a common large-scale evaluation task for audio event detection as well as a starting point for a comprehensive vocabulary of sound events.

For more details about AudioSet and the various models we have trained, please visit the AudioSet website and read our papers:

• Gemmeke, J. et. al., AudioSet: An ontology and human-labelled dataset for audio events, ICASSP 2017

• Hershey, S. et. al., CNN Architectures for Large-Scale Audio Classification, ICASSP 2017

If you use any of our pre-trained models in your published research, we ask that you cite CNN Architectures for Large-Scale Audio Classification. If you use the AudioSet dataset or the released embeddings of AudioSet segments, please cite AudioSet: An ontology and human-labelled dataset for audio events.

For general questions about AudioSet and these models, please use the audioset-users@googlegroups.com mailing list.

For technical problems with the released model and code, please open an issue on the tensorflow/models issue tracker and assign to @plakal and @dpwe. Please note that because the issue tracker is shared across all models released by Google, we won't be notified about an issue unless you explicitly @-mention us (@plakal and @dpwe) or assign the issue to us.

Original authors and reviewers of the code in this package include (in alphabetical order):

• DAn Ellis
• Shawn Hershey
• Aren Jansen
• Manoj Plakal